Conversion of the input image into a set of features is called feature extraction. In computer vision, the feature extraction process is a special form of dimensionality reduction, so that is the input data will be converted into a reduced representation set of features (called feature vector). The main goal of feature extraction is to obtain the most important information from the raw data and represent that information in a lower dimensionality space [1]}. In order to extract features, we used Pre-trained VGG-19 (a typical architecture is presented in Figure REF ), which was pre-trained on the ImageNet database [2]}. The obtained deep features were classified by some WEKA classifiers [3]}.
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